Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations79542
Missing cells467279
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 MiB
Average record size in memory328.0 B

Variable types

Numeric18
Text12
Categorical10
Unsupported1

Alerts

RUNDATE has constant value "45401.0" Constant
ACQDATE is highly overall correlated with MAINOFFHigh correlation
CBSA_DIV is highly overall correlated with CBSA_DIV_FLG and 13 other fieldsHigh correlation
CBSA_DIV_FLG is highly overall correlated with CBSA_DIV and 3 other fieldsHigh correlation
CBSA_DIV_NO is highly overall correlated with CBSA_DIV and 8 other fieldsHigh correlation
CBSA_METRO is highly overall correlated with CBSA_DIV and 6 other fieldsHigh correlation
CBSA_METRO_FLG is highly overall correlated with CBSA_DIV and 6 other fieldsHigh correlation
CBSA_MICRO_FLG is highly overall correlated with CBSA_DIV and 1 other fieldsHigh correlation
CBSA_NO is highly overall correlated with CBSA_DIV and 5 other fieldsHigh correlation
CERT is highly overall correlated with FI_UNINUMHigh correlation
CSA_FLG is highly overall correlated with CBSA_DIV and 6 other fieldsHigh correlation
CSA_NO is highly overall correlated with CBSA_DIV and 6 other fieldsHigh correlation
FI_UNINUM is highly overall correlated with CERTHigh correlation
ID is highly overall correlated with UNINUMHigh correlation
LATITUDE is highly overall correlated with CBSA_DIV and 1 other fieldsHigh correlation
LONGITUDE is highly overall correlated with CBSA_DIV and 2 other fieldsHigh correlation
MAINOFF is highly overall correlated with ACQDATEHigh correlation
MDI_STATUS_DESC is highly overall correlated with OFFNUMHigh correlation
OFFNUM is highly overall correlated with MDI_STATUS_DESCHigh correlation
SERVTYPE is highly overall correlated with SERVTYPE_DESCHigh correlation
SERVTYPE_DESC is highly overall correlated with SERVTYPEHigh correlation
STCNTY is highly overall correlated with CBSA_DIV and 3 other fieldsHigh correlation
UNINUM is highly overall correlated with IDHigh correlation
X is highly overall correlated with CBSA_DIV and 2 other fieldsHigh correlation
Y is highly overall correlated with CBSA_DIV and 1 other fieldsHigh correlation
ZIP is highly overall correlated with CBSA_DIV and 2 other fieldsHigh correlation
CBSA_MICRO_FLG is highly imbalanced (52.8%) Imbalance
MAINOFF is highly imbalanced (68.2%) Imbalance
SERVTYPE_DESC is highly imbalanced (84.0%) Imbalance
ACQDATE has 35323 (44.4%) missing values Missing
ADDRESS2 has 74814 (94.1%) missing values Missing
CBSA has 20151 (25.3%) missing values Missing
CBSA_DIV has 64418 (81.0%) missing values Missing
CBSA_DIV_NO has 44268 (55.7%) missing values Missing
CBSA_METRO_NAME has 28187 (35.4%) missing values Missing
CSA has 31357 (39.4%) missing values Missing
CSA_NO has 11207 (14.1%) missing values Missing
MDI_STATUS_CODE has 77963 (98.0%) missing values Missing
MDI_STATUS_DESC has 77963 (98.0%) missing values Missing
STALP has 803 (1.0%) missing values Missing
STNAME has 803 (1.0%) missing values Missing
OBJECTID is uniformly distributed Uniform
OBJECTID has unique values Unique
MDI_STATUS_CODE is an unsupported type, check if it needs cleaning or further analysis Unsupported
X has 5766 (7.2%) zeros Zeros
Y has 5766 (7.2%) zeros Zeros
CBSA_DIV_NO has 20151 (25.3%) zeros Zeros
CBSA_METRO has 28187 (35.4%) zeros Zeros
CBSA_NO has 20151 (25.3%) zeros Zeros
CSA_NO has 20151 (25.3%) zeros Zeros
LATITUDE has 5766 (7.2%) zeros Zeros
LONGITUDE has 5766 (7.2%) zeros Zeros
OFFNUM has 4577 (5.8%) zeros Zeros
STCNTY has 803 (1.0%) zeros Zeros

Reproduction

Analysis started2025-06-03 16:10:50.070850
Analysis finished2025-06-03 16:11:53.761757
Duration1 minute and 3.69 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

X
Real number (ℝ)

High correlation  Zeros 

Distinct72672
Distinct (%)91.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-83.77563
Minimum-166.24856
Maximum163.01083
Zeros5766
Zeros (%)7.2%
Negative73745
Negative (%)92.7%
Memory size621.6 KiB
2025-06-03T16:14:50.515607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.24856
5-th percentile-121.1656
Q1-95.931608
median-85.90069
Q3-77.369721
95-th percentile0
Maximum163.01083
Range329.25939
Interquartile range (IQR)18.561887

Descriptive statistics

Standard deviation27.774008
Coefficient of variation (CV)-0.33152849
Kurtosis5.2581445
Mean-83.77563
Median Absolute Deviation (MAD)9.340965
Skewness1.8487707
Sum-6663597.4
Variance771.39555
MonotonicityNot monotonic
2025-06-03T16:14:50.657387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5766
 
7.2%
-92.296582 11
 
< 0.1%
-112.0348945 9
 
< 0.1%
-66.059463 8
 
< 0.1%
-66.028075 8
 
< 0.1%
-93.27205096 6
 
< 0.1%
-98.495664 6
 
< 0.1%
-66.625687 6
 
< 0.1%
-96.80482098 6
 
< 0.1%
-85.49964299 5
 
< 0.1%
Other values (72662) 73710
92.7%
ValueCountFrequency (%)
-166.2485582 1
< 0.1%
-165.4092107 1
< 0.1%
-165.4082716 1
< 0.1%
-162.592615 1
< 0.1%
-161.759877 1
< 0.1%
-161.7517956 1
< 0.1%
-159.72357 1
< 0.1%
-159.585582 1
< 0.1%
-159.476557 1
< 0.1%
-159.4735174 1
< 0.1%
ValueCountFrequency (%)
163.01083 1
< 0.1%
158.2056 2
< 0.1%
151.84316 1
< 0.1%
151.84113 1
< 0.1%
145.760035 1
< 0.1%
145.7188384 2
< 0.1%
145.7172201 1
< 0.1%
145.6210308 1
< 0.1%
145.227823 1
< 0.1%
144.8843036 1
< 0.1%

Y
Real number (ℝ)

High correlation  Zeros 

Distinct72636
Distinct (%)91.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.080901
Minimum-24.892786
Maximum71.293282
Zeros5766
Zeros (%)7.2%
Negative1
Negative (%)< 0.1%
Memory size621.6 KiB
2025-06-03T16:14:50.804696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-24.892786
5-th percentile0
Q133.442709
median38.3252
Q341.248136
95-th percentile44.986226
Maximum71.293282
Range96.186068
Interquartile range (IQR)7.8054265

Descriptive statistics

Standard deviation11.030896
Coefficient of variation (CV)0.31444165
Kurtosis4.6456093
Mean35.080901
Median Absolute Deviation (MAD)3.797494
Skewness-2.2118584
Sum2790369.9
Variance121.68068
MonotonicityNot monotonic
2025-06-03T16:14:50.964935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5766
 
7.2%
38.97345401 11
 
< 0.1%
33.50985786 9
 
< 0.1%
18.347484 8
 
< 0.1%
18.210621 8
 
< 0.1%
29.42687902 6
 
< 0.1%
32.86271798 6
 
< 0.1%
44.97609999 6
 
< 0.1%
18.103866 6
 
< 0.1%
43.08686201 5
 
< 0.1%
Other values (72626) 73710
92.7%
ValueCountFrequency (%)
-24.89278582 1
 
< 0.1%
0 5766
7.2%
5.32492 1
 
< 0.1%
6.964 2
 
< 0.1%
7.34391 1
 
< 0.1%
7.34449 1
 
< 0.1%
7.44369 1
 
< 0.1%
7.44413 1
 
< 0.1%
13.39932359 1
 
< 0.1%
13.4378134 1
 
< 0.1%
ValueCountFrequency (%)
71.293282 1
< 0.1%
66.892278 1
< 0.1%
66.56444 1
< 0.1%
64.85844899 1
< 0.1%
64.85833001 1
< 0.1%
64.855525 1
< 0.1%
64.85161401 1
< 0.1%
64.85093201 1
< 0.1%
64.848722 1
< 0.1%
64.846762 1
< 0.1%

OBJECTID
Real number (ℝ)

Uniform  Unique 

Distinct79542
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39771.5
Minimum1
Maximum79542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:51.108470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3978.05
Q119886.25
median39771.5
Q359656.75
95-th percentile75564.95
Maximum79542
Range79541
Interquartile range (IQR)39770.5

Descriptive statistics

Standard deviation22961.942
Coefficient of variation (CV)0.57734664
Kurtosis-1.2
Mean39771.5
Median Absolute Deviation (MAD)19885.5
Skewness0
Sum3.1635047 × 109
Variance5.2725078 × 108
MonotonicityStrictly increasing
2025-06-03T16:14:51.283159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79542 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
79526 1
 
< 0.1%
79525 1
 
< 0.1%
79524 1
 
< 0.1%
79523 1
 
< 0.1%
Other values (79532) 79532
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
79542 1
< 0.1%
79541 1
< 0.1%
79540 1
< 0.1%
79539 1
< 0.1%
79538 1
< 0.1%
79537 1
< 0.1%
79536 1
< 0.1%
79535 1
< 0.1%
79534 1
< 0.1%
79533 1
< 0.1%

ACQDATE
Real number (ℝ)

High correlation  Missing 

Distinct3688
Distinct (%)8.3%
Missing35323
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean39808.9
Minimum25658
Maximum45383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:51.430766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25658
5-th percentile33798
Q137520.5
median40039
Q342651
95-th percentile44658
Maximum45383
Range19725
Interquartile range (IQR)5130.5

Descriptive statistics

Standard deviation3412.5757
Coefficient of variation (CV)0.085723937
Kurtosis-0.051323007
Mean39808.9
Median Absolute Deviation (MAD)2604
Skewness-0.45438499
Sum1.7603098 × 109
Variance11645673
MonotonicityNot monotonic
2025-06-03T16:14:51.583905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40257 2063
 
2.6%
36364 1127
 
1.4%
38304 1123
 
1.4%
39716 1115
 
1.4%
40123 763
 
1.0%
38516 759
 
1.0%
43806 745
 
0.9%
37112 646
 
0.8%
40086 632
 
0.8%
44477 539
 
0.7%
Other values (3678) 34707
43.6%
(Missing) 35323
44.4%
ValueCountFrequency (%)
25658 1
 
< 0.1%
25717 1
 
< 0.1%
25720 2
< 0.1%
25750 4
< 0.1%
25837 1
 
< 0.1%
25841 3
< 0.1%
25903 1
 
< 0.1%
25937 2
< 0.1%
25980 1
 
< 0.1%
26039 1
 
< 0.1%
ValueCountFrequency (%)
45383 67
0.1%
45370 5
 
< 0.1%
45352 14
 
< 0.1%
45344 1
 
< 0.1%
45339 6
 
< 0.1%
45338 7
 
< 0.1%
45334 2
 
< 0.1%
45332 8
 
< 0.1%
45331 4
 
< 0.1%
45324 2
 
< 0.1%
Distinct74294
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:52.019967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length73
Median length65
Mean length16.664089
Min length3

Characters and Unicode

Total characters1325495
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71617 ?
Unique (%)90.0%

Sample

1st row18001 Saint Rose Rd
2nd row1350 12th St
3rd row500 W Harrison St
4th row891 Fairfax St
5th row240 Salt Lick Rd
ValueCountFrequency (%)
st 24493
 
8.4%
ave 12833
 
4.4%
rd 12463
 
4.3%
n 8040
 
2.8%
w 7978
 
2.7%
s 7933
 
2.7%
main 7559
 
2.6%
e 7123
 
2.4%
blvd 6243
 
2.1%
dr 4518
 
1.5%
Other values (24052) 192899
66.0%
2025-06-03T16:14:52.600389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
212625
 
16.0%
e 72758
 
5.5%
t 63820
 
4.8%
1 60682
 
4.6%
a 59845
 
4.5%
0 55690
 
4.2%
n 47693
 
3.6%
r 45183
 
3.4%
S 43994
 
3.3%
i 41606
 
3.1%
Other values (65) 621599
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1325495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
212625
 
16.0%
e 72758
 
5.5%
t 63820
 
4.8%
1 60682
 
4.6%
a 59845
 
4.5%
0 55690
 
4.2%
n 47693
 
3.6%
r 45183
 
3.4%
S 43994
 
3.3%
i 41606
 
3.1%
Other values (65) 621599
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1325495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
212625
 
16.0%
e 72758
 
5.5%
t 63820
 
4.8%
1 60682
 
4.6%
a 59845
 
4.5%
0 55690
 
4.2%
n 47693
 
3.6%
r 45183
 
3.4%
S 43994
 
3.3%
i 41606
 
3.1%
Other values (65) 621599
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1325495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
212625
 
16.0%
e 72758
 
5.5%
t 63820
 
4.8%
1 60682
 
4.6%
a 59845
 
4.5%
0 55690
 
4.2%
n 47693
 
3.6%
r 45183
 
3.4%
S 43994
 
3.3%
i 41606
 
3.1%
Other values (65) 621599
46.9%

ADDRESS2
Text

Missing 

Distinct1302
Distinct (%)27.5%
Missing74814
Missing (%)94.1%
Memory size621.6 KiB
2025-06-03T16:14:52.975394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length7
Mean length7.4426819
Min length1

Characters and Unicode

Total characters35189
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique979 ?
Unique (%)20.7%

Sample

1st rowSte 104
2nd rowSte 150
3rd rowSte 300
4th rowSte D
5th rowSte 105
ValueCountFrequency (%)
ste 3691
37.3%
100 858
 
8.7%
101 275
 
2.8%
suite 262
 
2.6%
a 260
 
2.6%
223
 
2.3%
200 135
 
1.4%
unit 131
 
1.3%
1 127
 
1.3%
150 108
 
1.1%
Other values (1143) 3832
38.7%
2025-06-03T16:14:53.517295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5180
14.7%
0 4420
12.6%
t 4343
12.3%
e 4331
12.3%
S 4064
11.5%
1 3452
9.8%
2 1036
 
2.9%
5 653
 
1.9%
i 604
 
1.7%
3 530
 
1.5%
Other values (62) 6576
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5180
14.7%
0 4420
12.6%
t 4343
12.3%
e 4331
12.3%
S 4064
11.5%
1 3452
9.8%
2 1036
 
2.9%
5 653
 
1.9%
i 604
 
1.7%
3 530
 
1.5%
Other values (62) 6576
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5180
14.7%
0 4420
12.6%
t 4343
12.3%
e 4331
12.3%
S 4064
11.5%
1 3452
9.8%
2 1036
 
2.9%
5 653
 
1.9%
i 604
 
1.7%
3 530
 
1.5%
Other values (62) 6576
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5180
14.7%
0 4420
12.6%
t 4343
12.3%
e 4331
12.3%
S 4064
11.5%
1 3452
9.8%
2 1036
 
2.9%
5 653
 
1.9%
i 604
 
1.7%
3 530
 
1.5%
Other values (62) 6576
18.7%

BKCLASS
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
N
36757 
NM
25032 
SM
13308 
SI
 
2446
SB
 
1842
Other values (3)
 
157

Length

Max length7
Median length2
Mean length1.5379548
Min length1

Characters and Unicode

Total characters122332
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSM
2nd rowSM
3rd rowSM
4th rowSM
5th rowNM

Common Values

ValueCountFrequency (%)
N 36757
46.2%
NM 25032
31.5%
SM 13308
 
16.7%
SI 2446
 
3.1%
SB 1842
 
2.3%
SL 146
 
0.2%
OI 10
 
< 0.1%
BKCLASS 1
 
< 0.1%

Length

2025-06-03T16:14:53.636600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:14:53.741914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
n 36757
46.2%
nm 25032
31.5%
sm 13308
 
16.7%
si 2446
 
3.1%
sb 1842
 
2.3%
sl 146
 
0.2%
oi 10
 
< 0.1%
bkclass 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 61789
50.5%
M 38340
31.3%
S 17744
 
14.5%
I 2456
 
2.0%
B 1843
 
1.5%
L 147
 
0.1%
O 10
 
< 0.1%
K 1
 
< 0.1%
C 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61789
50.5%
M 38340
31.3%
S 17744
 
14.5%
I 2456
 
2.0%
B 1843
 
1.5%
L 147
 
0.1%
O 10
 
< 0.1%
K 1
 
< 0.1%
C 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61789
50.5%
M 38340
31.3%
S 17744
 
14.5%
I 2456
 
2.0%
B 1843
 
1.5%
L 147
 
0.1%
O 10
 
< 0.1%
K 1
 
< 0.1%
C 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61789
50.5%
M 38340
31.3%
S 17744
 
14.5%
I 2456
 
2.0%
B 1843
 
1.5%
L 147
 
0.1%
O 10
 
< 0.1%
K 1
 
< 0.1%
C 1
 
< 0.1%
A 1
 
< 0.1%

CBSA
Text

Missing 

Distinct823
Distinct (%)1.4%
Missing20151
Missing (%)25.3%
Memory size621.6 KiB
2025-06-03T16:14:53.997997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length34
Mean length24.396255
Min length4

Characters and Unicode

Total characters1448918
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowSt. Louis, MO-IL
2nd rowSt. Louis, MO-IL
3rd rowSt. Louis, MO-IL
4th rowSt. Louis, MO-IL
5th rowSt. Louis, MO-IL
ValueCountFrequency (%)
city 6526
 
4.3%
tx 5732
 
3.8%
new 4694
 
3.1%
york-newark-jersey 4299
 
2.8%
ny-nj-pa 4299
 
2.8%
fl 4287
 
2.8%
oh 2334
 
1.5%
il-in-wi 2252
 
1.5%
chicago-naperville-elgin 2252
 
1.5%
pa 2151
 
1.4%
Other values (922) 113084
74.4%
2025-06-03T16:14:54.386549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 94130
 
6.5%
- 92785
 
6.4%
92519
 
6.4%
e 91360
 
6.3%
o 78746
 
5.4%
n 76660
 
5.3%
r 71410
 
4.9%
i 65973
 
4.6%
l 61528
 
4.2%
, 59390
 
4.1%
Other values (47) 664417
45.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1448918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 94130
 
6.5%
- 92785
 
6.4%
92519
 
6.4%
e 91360
 
6.3%
o 78746
 
5.4%
n 76660
 
5.3%
r 71410
 
4.9%
i 65973
 
4.6%
l 61528
 
4.2%
, 59390
 
4.1%
Other values (47) 664417
45.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1448918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 94130
 
6.5%
- 92785
 
6.4%
92519
 
6.4%
e 91360
 
6.3%
o 78746
 
5.4%
n 76660
 
5.3%
r 71410
 
4.9%
i 65973
 
4.6%
l 61528
 
4.2%
, 59390
 
4.1%
Other values (47) 664417
45.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1448918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 94130
 
6.5%
- 92785
 
6.4%
92519
 
6.4%
e 91360
 
6.3%
o 78746
 
5.4%
n 76660
 
5.3%
r 71410
 
4.9%
i 65973
 
4.6%
l 61528
 
4.2%
, 59390
 
4.1%
Other values (47) 664417
45.9%

CBSA_DIV
Categorical

High correlation  Missing 

Distinct27
Distinct (%)0.2%
Missing64418
Missing (%)81.0%
Memory size621.6 KiB
New York-Jersey City-White Plains, NY-NJ
2392 
Chicago-Naperville-Evanston, IL
1679 
Dallas-Plano-Irving, TX
1129 
Washington-Arlington-Alexandria, DC-VA-MD-WV
951 
Cambridge-Newton-Framingham, MA
 
694
Other values (22)
8279 

Length

Max length49
Median length38
Mean length30.63343
Min length8

Characters and Unicode

Total characters463300
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCamden, NJ
2nd rowCamden, NJ
3rd rowMontgomery County-Bucks County-Chester County, PA
4th rowCamden, NJ
5th rowChicago-Naperville-Evanston, IL

Common Values

ValueCountFrequency (%)
New York-Jersey City-White Plains, NY-NJ 2392
 
3.0%
Chicago-Naperville-Evanston, IL 1679
 
2.1%
Dallas-Plano-Irving, TX 1129
 
1.4%
Washington-Arlington-Alexandria, DC-VA-MD-WV 951
 
1.2%
Cambridge-Newton-Framingham, MA 694
 
0.9%
Nassau County-Suffolk County, NY 657
 
0.8%
New Brunswick-Lakewood, NJ 648
 
0.8%
Seattle-Bellevue-Kent, WA 613
 
0.8%
Newark, NJ-PA 602
 
0.8%
Montgomery County-Bucks County-Chester County, PA 586
 
0.7%
Other values (17) 5173
 
6.5%
(Missing) 64418
81.0%

Length

2025-06-03T16:14:54.517131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 3040
 
6.6%
york-jersey 2392
 
5.2%
city-white 2392
 
5.2%
plains 2392
 
5.2%
ny-nj 2392
 
5.2%
il 1863
 
4.1%
chicago-naperville-evanston 1679
 
3.7%
tx 1646
 
3.6%
county 1555
 
3.4%
fl 1350
 
3.0%
Other values (51) 25026
54.7%

Most occurring characters

ValueCountFrequency (%)
e 30816
 
6.7%
30603
 
6.6%
- 30147
 
6.5%
a 29318
 
6.3%
n 27591
 
6.0%
o 24817
 
5.4%
i 24424
 
5.3%
t 21093
 
4.6%
r 20954
 
4.5%
l 18764
 
4.1%
Other values (39) 204773
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 463300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 30816
 
6.7%
30603
 
6.6%
- 30147
 
6.5%
a 29318
 
6.3%
n 27591
 
6.0%
o 24817
 
5.4%
i 24424
 
5.3%
t 21093
 
4.6%
r 20954
 
4.5%
l 18764
 
4.1%
Other values (39) 204773
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 463300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 30816
 
6.7%
30603
 
6.6%
- 30147
 
6.5%
a 29318
 
6.3%
n 27591
 
6.0%
o 24817
 
5.4%
i 24424
 
5.3%
t 21093
 
4.6%
r 20954
 
4.5%
l 18764
 
4.1%
Other values (39) 204773
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 463300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 30816
 
6.7%
30603
 
6.6%
- 30147
 
6.5%
a 29318
 
6.3%
n 27591
 
6.0%
o 24817
 
5.4%
i 24424
 
5.3%
t 21093
 
4.6%
r 20954
 
4.5%
l 18764
 
4.1%
Other values (39) 204773
44.2%

CBSA_DIV_FLG
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size621.6 KiB
0.0
64418 
1.0
15123 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters238623
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 64418
81.0%
1.0 15123
 
19.0%
(Missing) 1
 
< 0.1%

Length

2025-06-03T16:14:54.631148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:14:54.694360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 64418
81.0%
1.0 15123
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 143959
60.3%
. 79541
33.3%
1 15123
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 143959
60.3%
. 79541
33.3%
1 15123
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 143959
60.3%
. 79541
33.3%
1 15123
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 143959
60.3%
. 79541
33.3%
1 15123
 
6.3%

CBSA_DIV_NO
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct27
Distinct (%)0.1%
Missing44268
Missing (%)55.7%
Infinite0
Infinite (%)0.0%
Mean13093.458
Minimum0
Maximum48864
Zeros20151
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:54.811825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q329404
95-th percentile47664
Maximum48864
Range48864
Interquartile range (IQR)29404

Descriptive statistics

Standard deviation16743.616
Coefficient of variation (CV)1.2787772
Kurtosis-0.90882396
Mean13093.458
Median Absolute Deviation (MAD)0
Skewness0.79692815
Sum4.6185865 × 108
Variance2.8034868 × 108
MonotonicityNot monotonic
2025-06-03T16:14:54.992033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 20151
25.3%
35614 2392
 
3.0%
16984 1679
 
2.1%
19124 1129
 
1.4%
47894 951
 
1.2%
15764 694
 
0.9%
35004 657
 
0.8%
35154 648
 
0.8%
42644 613
 
0.8%
35084 602
 
0.8%
Other values (17) 5758
 
7.2%
(Missing) 44268
55.7%
ValueCountFrequency (%)
0 20151
25.3%
14454 575
 
0.7%
15764 694
 
0.9%
15804 262
 
0.3%
16984 1679
 
2.1%
19124 1129
 
1.4%
19804 252
 
0.3%
20994 184
 
0.2%
22744 373
 
0.5%
23104 517
 
0.6%
ValueCountFrequency (%)
48864 199
 
0.3%
48424 396
 
0.5%
47894 951
 
1.2%
47664 532
 
0.7%
45104 127
 
0.2%
42644 613
 
0.8%
40484 118
 
0.1%
37964 380
 
0.5%
35614 2392
3.0%
35154 648
 
0.8%

CBSA_METRO
Real number (ℝ)

High correlation  Zeros 

Distinct330
Distinct (%)0.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean19092.77
Minimum0
Maximum49660
Zeros28187
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:55.180706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17460
Q335620
95-th percentile45500
Maximum49660
Range49660
Interquartile range (IQR)35620

Descriptive statistics

Standard deviation16729.762
Coefficient of variation (CV)0.87623546
Kurtosis-1.4394915
Mean19092.77
Median Absolute Deviation (MAD)17460
Skewness0.15337292
Sum1.518658 × 109
Variance2.7988494 × 108
MonotonicityNot monotonic
2025-06-03T16:14:55.391939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28187
35.4%
35620 4299
 
5.4%
16980 2252
 
2.8%
19100 1646
 
2.1%
37980 1427
 
1.8%
14460 1387
 
1.7%
26420 1360
 
1.7%
33100 1350
 
1.7%
47900 1238
 
1.6%
12060 1071
 
1.3%
Other values (320) 35324
44.4%
ValueCountFrequency (%)
0 28187
35.4%
10180 47
 
0.1%
10380 15
 
< 0.1%
10420 165
 
0.2%
10500 37
 
< 0.1%
10540 20
 
< 0.1%
10580 272
 
0.3%
10740 138
 
0.2%
10780 59
 
0.1%
10900 194
 
0.2%
ValueCountFrequency (%)
49660 117
0.1%
49620 102
0.1%
49500 7
 
< 0.1%
49420 39
 
< 0.1%
49340 196
0.2%
49180 128
0.2%
49020 45
 
0.1%
48900 58
 
0.1%
48700 46
 
0.1%
48660 38
 
< 0.1%

CBSA_METRO_FLG
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size621.6 KiB
1.0
51354 
0.0
28187 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters238623
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 51354
64.6%
0.0 28187
35.4%
(Missing) 1
 
< 0.1%

Length

2025-06-03T16:14:55.557992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:14:55.647854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 51354
64.6%
0.0 28187
35.4%

Most occurring characters

ValueCountFrequency (%)
0 107728
45.1%
. 79541
33.3%
1 51354
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 107728
45.1%
. 79541
33.3%
1 51354
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 107728
45.1%
. 79541
33.3%
1 51354
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 107728
45.1%
. 79541
33.3%
1 51354
21.5%

CBSA_METRO_NAME
Text

Missing 

Distinct330
Distinct (%)0.6%
Missing28187
Missing (%)35.4%
Memory size621.6 KiB
2025-06-03T16:14:55.922247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length33
Mean length26.136579
Min length8

Characters and Unicode

Total characters1342244
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSt. Louis, MO-IL
2nd rowSt. Louis, MO-IL
3rd rowSt. Louis, MO-IL
4th rowSt. Louis, MO-IL
5th rowSt. Louis, MO-IL
ValueCountFrequency (%)
city 6268
 
4.7%
tx 5237
 
3.9%
new 4619
 
3.4%
york-newark-jersey 4299
 
3.2%
ny-nj-pa 4299
 
3.2%
fl 4213
 
3.1%
il-in-wi 2252
 
1.7%
chicago-naperville-elgin 2252
 
1.7%
pa 1815
 
1.4%
oh 1734
 
1.3%
Other values (460) 97386
72.5%
2025-06-03T16:14:56.450826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 91806
 
6.8%
a 88095
 
6.6%
e 84320
 
6.3%
83019
 
6.2%
o 73512
 
5.5%
n 70904
 
5.3%
r 66684
 
5.0%
i 61287
 
4.6%
l 56478
 
4.2%
t 54828
 
4.1%
Other values (45) 611311
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1342244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 91806
 
6.8%
a 88095
 
6.6%
e 84320
 
6.3%
83019
 
6.2%
o 73512
 
5.5%
n 70904
 
5.3%
r 66684
 
5.0%
i 61287
 
4.6%
l 56478
 
4.2%
t 54828
 
4.1%
Other values (45) 611311
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1342244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 91806
 
6.8%
a 88095
 
6.6%
e 84320
 
6.3%
83019
 
6.2%
o 73512
 
5.5%
n 70904
 
5.3%
r 66684
 
5.0%
i 61287
 
4.6%
l 56478
 
4.2%
t 54828
 
4.1%
Other values (45) 611311
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1342244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 91806
 
6.8%
a 88095
 
6.6%
e 84320
 
6.3%
83019
 
6.2%
o 73512
 
5.5%
n 70904
 
5.3%
r 66684
 
5.0%
i 61287
 
4.6%
l 56478
 
4.2%
t 54828
 
4.1%
Other values (45) 611311
45.5%

CBSA_MICRO_FLG
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size621.6 KiB
0.0
71505 
1.0
8036 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters238623
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 71505
89.9%
1.0 8036
 
10.1%
(Missing) 1
 
< 0.1%

Length

2025-06-03T16:14:56.617973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:14:56.719875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 71505
89.9%
1.0 8036
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 151046
63.3%
. 79541
33.3%
1 8036
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 151046
63.3%
. 79541
33.3%
1 8036
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 151046
63.3%
. 79541
33.3%
1 8036
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 151046
63.3%
. 79541
33.3%
1 8036
 
3.4%

CBSA_NO
Real number (ℝ)

High correlation  Zeros 

Distinct823
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22142.574
Minimum0
Maximum49820
Zeros20151
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:56.874385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22800
Q335620
95-th percentile46140
Maximum49820
Range49820
Interquartile range (IQR)35620

Descriptive statistics

Standard deviation16085.458
Coefficient of variation (CV)0.72644931
Kurtosis-1.3133933
Mean22142.574
Median Absolute Deviation (MAD)12820
Skewness-0.1023125
Sum1.7612425 × 109
Variance2.5874196 × 108
MonotonicityNot monotonic
2025-06-03T16:14:57.098170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20151
25.3%
35620 4299
 
5.4%
16980 2252
 
2.8%
19100 1646
 
2.1%
37980 1427
 
1.8%
14460 1387
 
1.7%
26420 1360
 
1.7%
33100 1350
 
1.7%
47900 1238
 
1.6%
12060 1071
 
1.3%
Other values (813) 43360
54.5%
ValueCountFrequency (%)
0 20151
25.3%
10100 20
 
< 0.1%
10140 23
 
< 0.1%
10180 47
 
0.1%
10220 15
 
< 0.1%
10300 18
 
< 0.1%
10380 15
 
< 0.1%
10420 165
 
0.2%
10460 13
 
< 0.1%
10500 37
 
< 0.1%
ValueCountFrequency (%)
49820 3
 
< 0.1%
49780 27
 
< 0.1%
49660 117
0.1%
49620 102
0.1%
49500 7
 
< 0.1%
49460 9
 
< 0.1%
49420 39
 
< 0.1%
49380 13
 
< 0.1%
49340 196
0.2%
49300 43
 
0.1%

CERT
Real number (ℝ)

High correlation 

Distinct4577
Distinct (%)5.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14436.781
Minimum14
Maximum91325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:57.320714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile628
Q13511
median9712
Q318609
95-th percentile57415
Maximum91325
Range91311
Interquartile range (IQR)15098

Descriptive statistics

Standard deviation14942.208
Coefficient of variation (CV)1.0350097
Kurtosis4.600371
Mean14436.781
Median Absolute Deviation (MAD)6427
Skewness1.9460052
Sum1.148316 × 109
Variance2.2326958 × 108
MonotonicityNot monotonic
2025-06-03T16:14:57.531332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
628 5141
 
6.5%
3511 4366
 
5.5%
3510 3977
 
5.0%
6384 2375
 
3.0%
6548 2299
 
2.9%
9846 2005
 
2.5%
12368 1282
 
1.6%
18409 1184
 
1.5%
6560 1177
 
1.5%
6672 1079
 
1.4%
Other values (4567) 54656
68.7%
ValueCountFrequency (%)
14 3
 
< 0.1%
35 8
 
< 0.1%
39 8
 
< 0.1%
41 4
 
< 0.1%
49 1
 
< 0.1%
50 7
 
< 0.1%
51 5
 
< 0.1%
52 1
 
< 0.1%
54 5
 
< 0.1%
58 21
< 0.1%
ValueCountFrequency (%)
91325 15
< 0.1%
91280 5
 
< 0.1%
91005 6
 
< 0.1%
90384 4
 
< 0.1%
90311 7
 
< 0.1%
90308 6
 
< 0.1%
90303 13
< 0.1%
90300 27
< 0.1%
90297 7
 
< 0.1%
90291 9
 
< 0.1%

CITY
Text

Distinct10354
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:57.864686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length25
Mean length8.8637324
Min length3

Characters and Unicode

Total characters705039
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3716 ?
Unique (%)4.7%

Sample

1st rowBreese
2nd rowCarlyle
3rd rowAviston
4th rowCarlyle
5th rowSaint Peters
ValueCountFrequency (%)
city 2141
 
2.1%
new 1217
 
1.2%
san 1168
 
1.2%
beach 946
 
0.9%
park 786
 
0.8%
fort 755
 
0.7%
lake 722
 
0.7%
york 663
 
0.7%
west 660
 
0.7%
saint 643
 
0.6%
Other values (8480) 91511
90.4%
2025-06-03T16:14:58.311545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 64102
 
9.1%
a 60487
 
8.6%
o 54155
 
7.7%
n 53613
 
7.6%
l 47976
 
6.8%
i 44204
 
6.3%
r 44143
 
6.3%
t 37189
 
5.3%
s 30731
 
4.4%
21671
 
3.1%
Other values (55) 246768
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 705039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 64102
 
9.1%
a 60487
 
8.6%
o 54155
 
7.7%
n 53613
 
7.6%
l 47976
 
6.8%
i 44204
 
6.3%
r 44143
 
6.3%
t 37189
 
5.3%
s 30731
 
4.4%
21671
 
3.1%
Other values (55) 246768
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 705039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 64102
 
9.1%
a 60487
 
8.6%
o 54155
 
7.7%
n 53613
 
7.6%
l 47976
 
6.8%
i 44204
 
6.3%
r 44143
 
6.3%
t 37189
 
5.3%
s 30731
 
4.4%
21671
 
3.1%
Other values (55) 246768
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 705039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 64102
 
9.1%
a 60487
 
8.6%
o 54155
 
7.7%
n 53613
 
7.6%
l 47976
 
6.8%
i 44204
 
6.3%
r 44143
 
6.3%
t 37189
 
5.3%
s 30731
 
4.4%
21671
 
3.1%
Other values (55) 246768
35.0%

COUNTY
Text

Distinct1914
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:14:58.661415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length19
Mean length7.4486058
Min length3

Characters and Unicode

Total characters592477
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)0.2%

Sample

1st rowClinton
2nd rowClinton
3rd rowClinton
4th rowClinton
5th rowSt. Charles
ValueCountFrequency (%)
los 1471
 
1.6%
angeles 1466
 
1.6%
san 1355
 
1.5%
cook 1162
 
1.3%
st 1083
 
1.2%
new 1067
 
1.2%
orange 977
 
1.1%
montgomery 973
 
1.1%
jefferson 947
 
1.0%
harris 888
 
1.0%
Other values (1927) 78951
87.4%
2025-06-03T16:14:59.164663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 60033
 
10.1%
e 55815
 
9.4%
n 46011
 
7.8%
o 45147
 
7.6%
r 38114
 
6.4%
l 33275
 
5.6%
i 32248
 
5.4%
s 28551
 
4.8%
t 24319
 
4.1%
u 15271
 
2.6%
Other values (47) 213693
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 592477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 60033
 
10.1%
e 55815
 
9.4%
n 46011
 
7.8%
o 45147
 
7.6%
r 38114
 
6.4%
l 33275
 
5.6%
i 32248
 
5.4%
s 28551
 
4.8%
t 24319
 
4.1%
u 15271
 
2.6%
Other values (47) 213693
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 592477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 60033
 
10.1%
e 55815
 
9.4%
n 46011
 
7.8%
o 45147
 
7.6%
r 38114
 
6.4%
l 33275
 
5.6%
i 32248
 
5.4%
s 28551
 
4.8%
t 24319
 
4.1%
u 15271
 
2.6%
Other values (47) 213693
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 592477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 60033
 
10.1%
e 55815
 
9.4%
n 46011
 
7.8%
o 45147
 
7.6%
r 38114
 
6.4%
l 33275
 
5.6%
i 32248
 
5.4%
s 28551
 
4.8%
t 24319
 
4.1%
u 15271
 
2.6%
Other values (47) 213693
36.1%

CSA
Text

Missing 

Distinct155
Distinct (%)0.3%
Missing31357
Missing (%)39.4%
Memory size621.6 KiB
2025-06-03T16:14:59.411436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length58
Median length41
Mean length31.908519
Min length3

Characters and Unicode

Total characters1537512
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSt. Louis-St. Charles-Farmington, MO-IL
2nd rowSt. Louis-St. Charles-Farmington, MO-IL
3rd rowSt. Louis-St. Charles-Farmington, MO-IL
4th rowSt. Louis-St. Charles-Farmington, MO-IL
5th rowSt. Louis-St. Charles-Farmington, MO-IL
ValueCountFrequency (%)
new 5058
 
3.9%
ny-nj-ct-pa 4661
 
3.6%
york-newark 4661
 
3.6%
fl 2896
 
2.2%
tx 2639
 
2.0%
il-in-wi 2386
 
1.8%
chicago-naperville 2386
 
1.8%
st 2382
 
1.8%
boston-worcester-providence 2212
 
1.7%
ma-ri-nh-ct 2212
 
1.7%
Other values (294) 99105
75.9%
2025-06-03T16:14:59.804461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 132236
 
8.6%
a 99827
 
6.5%
e 99057
 
6.4%
o 84261
 
5.5%
n 83220
 
5.4%
82413
 
5.4%
r 76193
 
5.0%
i 66479
 
4.3%
t 64955
 
4.2%
l 63105
 
4.1%
Other values (46) 685766
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1537512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 132236
 
8.6%
a 99827
 
6.5%
e 99057
 
6.4%
o 84261
 
5.5%
n 83220
 
5.4%
82413
 
5.4%
r 76193
 
5.0%
i 66479
 
4.3%
t 64955
 
4.2%
l 63105
 
4.1%
Other values (46) 685766
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1537512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 132236
 
8.6%
a 99827
 
6.5%
e 99057
 
6.4%
o 84261
 
5.5%
n 83220
 
5.4%
82413
 
5.4%
r 76193
 
5.0%
i 66479
 
4.3%
t 64955
 
4.2%
l 63105
 
4.1%
Other values (46) 685766
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1537512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 132236
 
8.6%
a 99827
 
6.5%
e 99057
 
6.4%
o 84261
 
5.5%
n 83220
 
5.4%
82413
 
5.4%
r 76193
 
5.0%
i 66479
 
4.3%
t 64955
 
4.2%
l 63105
 
4.1%
Other values (46) 685766
44.6%

CSA_FLG
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size621.6 KiB
1.0
48184 
0.0
31357 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters238623
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 48184
60.6%
0.0 31357
39.4%
(Missing) 1
 
< 0.1%

Length

2025-06-03T16:14:59.910117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:14:59.977718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 48184
60.6%
0.0 31357
39.4%

Most occurring characters

ValueCountFrequency (%)
0 110898
46.5%
. 79541
33.3%
1 48184
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 110898
46.5%
. 79541
33.3%
1 48184
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 110898
46.5%
. 79541
33.3%
1 48184
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 110898
46.5%
. 79541
33.3%
1 48184
20.2%

CSA_NO
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct155
Distinct (%)0.2%
Missing11207
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean232.08586
Minimum0
Maximum566
Zeros20151
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:00.091464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median212
Q3408
95-th percentile534
Maximum566
Range566
Interquartile range (IQR)408

Descriptive statistics

Standard deviation185.23903
Coefficient of variation (CV)0.79814872
Kurtosis-1.3672762
Mean232.08586
Median Absolute Deviation (MAD)196
Skewness0.055114421
Sum15859587
Variance34313.498
MonotonicityNot monotonic
2025-06-03T16:15:00.234875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20151
25.3%
408 4661
 
5.9%
176 2386
 
3.0%
148 2212
 
2.8%
548 1965
 
2.5%
206 1797
 
2.3%
428 1661
 
2.1%
370 1515
 
1.9%
288 1407
 
1.8%
122 1232
 
1.5%
Other values (145) 29348
36.9%
(Missing) 11207
 
14.1%
ValueCountFrequency (%)
0 20151
25.3%
104 357
 
0.4%
106 189
 
0.2%
107 61
 
0.1%
108 81
 
0.1%
118 81
 
0.1%
120 109
 
0.1%
122 1232
 
1.5%
140 50
 
0.1%
144 49
 
0.1%
ValueCountFrequency (%)
566 149
 
0.2%
558 58
 
0.1%
556 236
 
0.3%
554 102
 
0.1%
548 1965
2.5%
545 272
 
0.3%
544 34
 
< 0.1%
540 101
 
0.1%
539 91
 
0.1%
538 311
 
0.4%

ESTYMD
Text

Distinct26127
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:00.575840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.3876443
Min length1

Characters and Unicode

Total characters746712
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13497 ?
Unique (%)17.0%

Sample

1st row30964
2nd row25373
3rd row39104
4th row01/01/1878
5th row38383
ValueCountFrequency (%)
01/01/1889 312
 
0.4%
01/01/1890 249
 
0.3%
01/01/1934 233
 
0.3%
01/01/1919 232
 
0.3%
01/01/1923 232
 
0.3%
01/01/1887 205
 
0.3%
01/01/1921 159
 
0.2%
10/23/1989 156
 
0.2%
06/30/1987 153
 
0.2%
06/30/1985 148
 
0.2%
Other values (26117) 77463
97.4%
2025-06-03T16:15:01.034165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 152117
20.4%
/ 139934
18.7%
1 134962
18.1%
2 81802
11.0%
9 69713
9.3%
8 33477
 
4.5%
3 31566
 
4.2%
7 27990
 
3.7%
6 26864
 
3.6%
5 24649
 
3.3%
Other values (7) 23638
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 746712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 152117
20.4%
/ 139934
18.7%
1 134962
18.1%
2 81802
11.0%
9 69713
9.3%
8 33477
 
4.5%
3 31566
 
4.2%
7 27990
 
3.7%
6 26864
 
3.6%
5 24649
 
3.3%
Other values (7) 23638
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 746712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 152117
20.4%
/ 139934
18.7%
1 134962
18.1%
2 81802
11.0%
9 69713
9.3%
8 33477
 
4.5%
3 31566
 
4.2%
7 27990
 
3.7%
6 26864
 
3.6%
5 24649
 
3.3%
Other values (7) 23638
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 746712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 152117
20.4%
/ 139934
18.7%
1 134962
18.1%
2 81802
11.0%
9 69713
9.3%
8 33477
 
4.5%
3 31566
 
4.2%
7 27990
 
3.7%
6 26864
 
3.6%
5 24649
 
3.3%
Other values (7) 23638
 
3.2%

FI_UNINUM
Real number (ℝ)

High correlation 

Distinct4577
Distinct (%)5.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean32378.417
Minimum6
Maximum650140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:01.172772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile417
Q12239
median6212
Q312494
95-th percentile82012
Maximum650140
Range650134
Interquartile range (IQR)10255

Descriptive statistics

Standard deviation94083.084
Coefficient of variation (CV)2.9057345
Kurtosis15.521056
Mean32378.417
Median Absolute Deviation (MAD)4088
Skewness4.0901547
Sum2.5754117 × 109
Variance8.8516266 × 109
MonotonicityNot monotonic
2025-06-03T16:15:01.326063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
417 5141
 
6.5%
2239 4366
 
5.5%
2238 3977
 
5.0%
4287 2375
 
3.0%
4383 2299
 
2.9%
6300 2005
 
2.5%
7866 1282
 
1.6%
12315 1184
 
1.5%
4390 1177
 
1.5%
4470 1079
 
1.4%
Other values (4567) 54656
68.7%
ValueCountFrequency (%)
6 3
 
< 0.1%
17 8
 
< 0.1%
21 8
 
< 0.1%
23 4
 
< 0.1%
30 1
 
< 0.1%
31 7
 
< 0.1%
32 5
 
< 0.1%
33 1
 
< 0.1%
35 5
 
< 0.1%
39 21
< 0.1%
ValueCountFrequency (%)
650140 1
 
< 0.1%
649963 1
 
< 0.1%
647694 1
 
< 0.1%
645970 1
 
< 0.1%
644984 4
< 0.1%
641772 2
< 0.1%
641744 1
 
< 0.1%
641636 1
 
< 0.1%
640962 1
 
< 0.1%
640695 1
 
< 0.1%

ID
Real number (ℝ)

High correlation 

Distinct79541
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean305329.74
Minimum3
Maximum663907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:01.463497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6214
Q1198678
median263437
Q3464262
95-th percentile627297
Maximum663907
Range663904
Interquartile range (IQR)265584

Descriptive statistics

Standard deviation191723.6
Coefficient of variation (CV)0.62792309
Kurtosis-0.95377428
Mean305329.74
Median Absolute Deviation (MAD)168927
Skewness0.08674046
Sum2.4286233 × 1010
Variance3.6757937 × 1010
MonotonicityNot monotonic
2025-06-03T16:15:01.606113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
629595 1
 
< 0.1%
223055 1
 
< 0.1%
232078 1
 
< 0.1%
466427 1
 
< 0.1%
9231 1
 
< 0.1%
429739 1
 
< 0.1%
641411 1
 
< 0.1%
641392 1
 
< 0.1%
632193 1
 
< 0.1%
621883 1
 
< 0.1%
Other values (79531) 79531
> 99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
ValueCountFrequency (%)
663907 1
< 0.1%
663906 1
< 0.1%
663905 1
< 0.1%
663904 1
< 0.1%
663898 1
< 0.1%
663897 1
< 0.1%
663896 1
< 0.1%
663886 1
< 0.1%
663885 1
< 0.1%
663876 1
< 0.1%

LATITUDE
Real number (ℝ)

High correlation  Zeros 

Distinct72751
Distinct (%)91.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.080901
Minimum-24.892786
Maximum71.293282
Zeros5766
Zeros (%)7.2%
Negative1
Negative (%)< 0.1%
Memory size621.6 KiB
2025-06-03T16:15:01.764535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-24.892786
5-th percentile0
Q133.442709
median38.3252
Q341.248136
95-th percentile44.986226
Maximum71.293282
Range96.186068
Interquartile range (IQR)7.8054265

Descriptive statistics

Standard deviation11.030896
Coefficient of variation (CV)0.31444165
Kurtosis4.6456093
Mean35.080901
Median Absolute Deviation (MAD)3.797494
Skewness-2.2118584
Sum2790369.9
Variance121.68068
MonotonicityNot monotonic
2025-06-03T16:15:03.496529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5766
 
7.2%
38.97345401 11
 
< 0.1%
33.50985786 9
 
< 0.1%
18.210621 8
 
< 0.1%
18.347484 8
 
< 0.1%
18.103866 6
 
< 0.1%
29.42687902 6
 
< 0.1%
44.97609999 6
 
< 0.1%
43.08686201 5
 
< 0.1%
42.934779 5
 
< 0.1%
Other values (72741) 73711
92.7%
ValueCountFrequency (%)
-24.89278582 1
 
< 0.1%
0 5766
7.2%
5.32492 1
 
< 0.1%
6.964 2
 
< 0.1%
7.34391 1
 
< 0.1%
7.34449 1
 
< 0.1%
7.44369 1
 
< 0.1%
7.44413 1
 
< 0.1%
13.39932359 1
 
< 0.1%
13.4378134 1
 
< 0.1%
ValueCountFrequency (%)
71.293282 1
< 0.1%
66.892278 1
< 0.1%
66.56444 1
< 0.1%
64.85844899 1
< 0.1%
64.85833001 1
< 0.1%
64.855525 1
< 0.1%
64.85161401 1
< 0.1%
64.85093201 1
< 0.1%
64.848722 1
< 0.1%
64.846762 1
< 0.1%

LONGITUDE
Real number (ℝ)

High correlation  Zeros 

Distinct72774
Distinct (%)91.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-83.77563
Minimum-166.24856
Maximum163.01083
Zeros5766
Zeros (%)7.2%
Negative73745
Negative (%)92.7%
Memory size621.6 KiB
2025-06-03T16:15:03.633615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.24856
5-th percentile-121.1656
Q1-95.931608
median-85.90069
Q3-77.369721
95-th percentile0
Maximum163.01083
Range329.25939
Interquartile range (IQR)18.561887

Descriptive statistics

Standard deviation27.774008
Coefficient of variation (CV)-0.33152849
Kurtosis5.2581445
Mean-83.77563
Median Absolute Deviation (MAD)9.340965
Skewness1.8487707
Sum-6663597.4
Variance771.39555
MonotonicityNot monotonic
2025-06-03T16:15:03.884439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5766
 
7.2%
-92.296582 11
 
< 0.1%
-112.0348945 9
 
< 0.1%
-66.028075 8
 
< 0.1%
-66.059463 8
 
< 0.1%
-98.495664 6
 
< 0.1%
-66.625687 6
 
< 0.1%
-93.27205096 5
 
< 0.1%
-85.51531002 5
 
< 0.1%
-85.49964299 5
 
< 0.1%
Other values (72764) 73712
92.7%
ValueCountFrequency (%)
-166.2485582 1
< 0.1%
-165.4092107 1
< 0.1%
-165.4082716 1
< 0.1%
-162.592615 1
< 0.1%
-161.759877 1
< 0.1%
-161.7517956 1
< 0.1%
-159.72357 1
< 0.1%
-159.585582 1
< 0.1%
-159.476557 1
< 0.1%
-159.4735174 1
< 0.1%
ValueCountFrequency (%)
163.01083 1
< 0.1%
158.2056 2
< 0.1%
151.84316 1
< 0.1%
151.84113 1
< 0.1%
145.760035 1
< 0.1%
145.7188384 2
< 0.1%
145.7172201 1
< 0.1%
145.6210308 1
< 0.1%
145.227823 1
< 0.1%
144.8843036 1
< 0.1%

MAINOFF
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size621.6 KiB
0.0
74964 
1.0
 
4577

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters238623
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 74964
94.2%
1.0 4577
 
5.8%
(Missing) 1
 
< 0.1%

Length

2025-06-03T16:15:04.047436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:15:04.118535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 74964
94.2%
1.0 4577
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 154505
64.7%
. 79541
33.3%
1 4577
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154505
64.7%
. 79541
33.3%
1 4577
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154505
64.7%
. 79541
33.3%
1 4577
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154505
64.7%
. 79541
33.3%
1 4577
 
1.9%

MDI_STATUS_CODE
Unsupported

Missing  Rejected  Unsupported 

Missing77963
Missing (%)98.0%
Memory size621.6 KiB

MDI_STATUS_DESC
Categorical

High correlation  Missing 

Distinct11
Distinct (%)0.7%
Missing77963
Missing (%)98.0%
Memory size621.6 KiB
MINORITY BOARD AND SERVING HISPANIC COMMUNITY
510 
ASIAN OR PACIFIC ISLANDER AMERICANS
409 
MINORITY BOARD AND SERVING ASIAN OR PACIFIC ISLANDER COMMUNITY
310 
HISPANIC AMERICAN
134 
NATIVE AMERICAN OR NATIVE ALASKAN AMERICAN
105 
Other values (6)
111 

Length

Max length62
Median length53
Mean length41.644712
Min length4

Characters and Unicode

Total characters65757
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowASIAN OR PACIFIC ISLANDER AMERICANS
2nd rowASIAN OR PACIFIC ISLANDER AMERICANS
3rd rowASIAN OR PACIFIC ISLANDER AMERICANS
4th rowASIAN OR PACIFIC ISLANDER AMERICANS
5th rowASIAN OR PACIFIC ISLANDER AMERICANS

Common Values

ValueCountFrequency (%)
MINORITY BOARD AND SERVING HISPANIC COMMUNITY 510
 
0.6%
ASIAN OR PACIFIC ISLANDER AMERICANS 409
 
0.5%
MINORITY BOARD AND SERVING ASIAN OR PACIFIC ISLANDER COMMUNITY 310
 
0.4%
HISPANIC AMERICAN 134
 
0.2%
NATIVE AMERICAN OR NATIVE ALASKAN AMERICAN 105
 
0.1%
AFRICAN AMERICAN 79
 
0.1%
MINORITY BOARD AND SERVING AFRICAN AMERICAN COMMUNITY 21
 
< 0.1%
NONE 6
 
< 0.1%
MINORITY BOARD AND SERVING MULTI-RACIAL COMMUNITY 3
 
< 0.1%
MDI_STATUS_DESC 1
 
< 0.1%
(Missing) 77963
98.0%

Length

2025-06-03T16:15:04.211483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
minority 844
9.2%
board 844
9.2%
and 844
9.2%
serving 844
9.2%
community 844
9.2%
or 824
9.0%
pacific 719
7.9%
asian 719
7.9%
islander 719
7.9%
hispanic 644
7.1%
Other values (8) 1280
14.0%

Most occurring characters

ValueCountFrequency (%)
I 8713
13.3%
A 7650
11.6%
7546
11.5%
N 6739
10.2%
R 5033
 
7.7%
C 3885
 
5.9%
S 3443
 
5.2%
M 3391
 
5.2%
O 3362
 
5.1%
E 2634
 
4.0%
Other values (14) 13361
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 8713
13.3%
A 7650
11.6%
7546
11.5%
N 6739
10.2%
R 5033
 
7.7%
C 3885
 
5.9%
S 3443
 
5.2%
M 3391
 
5.2%
O 3362
 
5.1%
E 2634
 
4.0%
Other values (14) 13361
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 8713
13.3%
A 7650
11.6%
7546
11.5%
N 6739
10.2%
R 5033
 
7.7%
C 3885
 
5.9%
S 3443
 
5.2%
M 3391
 
5.2%
O 3362
 
5.1%
E 2634
 
4.0%
Other values (14) 13361
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 8713
13.3%
A 7650
11.6%
7546
11.5%
N 6739
10.2%
R 5033
 
7.7%
C 3885
 
5.9%
S 3443
 
5.2%
M 3391
 
5.2%
O 3362
 
5.1%
E 2634
 
4.0%
Other values (14) 13361
20.3%

NAME
Text

Distinct4016
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:04.428762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length68
Median length56
Mean length25.620867
Min length3

Characters and Unicode

Total characters2037935
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique704 ?
Unique (%)0.9%

Sample

1st row1NB Bank
2nd row1NB Bank
3rd row1NB Bank
4th row1NB Bank
5th row1st Advantage Bank
ValueCountFrequency (%)
bank 72321
24.8%
national 35733
 
12.2%
association 28951
 
9.9%
of 9981
 
3.4%
first 6776
 
2.3%
trust 6207
 
2.1%
the 5258
 
1.8%
chase 5142
 
1.8%
jpmorgan 5142
 
1.8%
wells 4375
 
1.5%
Other values (2681) 112192
38.4%
2025-06-03T16:15:04.875530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 247526
12.1%
212925
 
10.4%
n 202241
 
9.9%
i 151721
 
7.4%
o 150523
 
7.4%
t 125249
 
6.1%
s 112556
 
5.5%
B 81439
 
4.0%
k 78621
 
3.9%
e 72465
 
3.6%
Other values (61) 602669
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2037935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 247526
12.1%
212925
 
10.4%
n 202241
 
9.9%
i 151721
 
7.4%
o 150523
 
7.4%
t 125249
 
6.1%
s 112556
 
5.5%
B 81439
 
4.0%
k 78621
 
3.9%
e 72465
 
3.6%
Other values (61) 602669
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2037935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 247526
12.1%
212925
 
10.4%
n 202241
 
9.9%
i 151721
 
7.4%
o 150523
 
7.4%
t 125249
 
6.1%
s 112556
 
5.5%
B 81439
 
4.0%
k 78621
 
3.9%
e 72465
 
3.6%
Other values (61) 602669
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2037935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 247526
12.1%
212925
 
10.4%
n 202241
 
9.9%
i 151721
 
7.4%
o 150523
 
7.4%
t 125249
 
6.1%
s 112556
 
5.5%
B 81439
 
4.0%
k 78621
 
3.9%
e 72465
 
3.6%
Other values (61) 602669
29.6%
Distinct51238
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:05.321114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length93
Median length69
Mean length20.07865
Min length3

Characters and Unicode

Total characters1597096
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42175 ?
Unique (%)53.0%

Sample

1st rowST. ROSE FACILITY BRANCH
2nd row1350 12TH STREET FACILITY
3rd rowAVISTON BRANCH
4th row1NB Bank
5th row1st Advantage Bank
ValueCountFrequency (%)
branch 69727
29.2%
bank 6361
 
2.7%
3614
 
1.5%
center 2868
 
1.2%
street 2211
 
0.9%
main 2039
 
0.9%
and 1917
 
0.8%
office 1871
 
0.8%
west 1787
 
0.7%
banking 1755
 
0.7%
Other values (18828) 144855
60.6%
2025-06-03T16:15:05.948402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
160059
 
10.0%
A 135262
 
8.5%
N 127057
 
8.0%
R 120499
 
7.5%
B 93985
 
5.9%
C 91654
 
5.7%
H 84757
 
5.3%
E 82327
 
5.2%
O 57740
 
3.6%
T 53707
 
3.4%
Other values (71) 590049
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1597096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
160059
 
10.0%
A 135262
 
8.5%
N 127057
 
8.0%
R 120499
 
7.5%
B 93985
 
5.9%
C 91654
 
5.7%
H 84757
 
5.3%
E 82327
 
5.2%
O 57740
 
3.6%
T 53707
 
3.4%
Other values (71) 590049
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1597096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
160059
 
10.0%
A 135262
 
8.5%
N 127057
 
8.0%
R 120499
 
7.5%
B 93985
 
5.9%
C 91654
 
5.7%
H 84757
 
5.3%
E 82327
 
5.2%
O 57740
 
3.6%
T 53707
 
3.4%
Other values (71) 590049
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1597096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
160059
 
10.0%
A 135262
 
8.5%
N 127057
 
8.0%
R 120499
 
7.5%
B 93985
 
5.9%
C 91654
 
5.7%
H 84757
 
5.3%
E 82327
 
5.2%
O 57740
 
3.6%
T 53707
 
3.4%
Other values (71) 590049
36.9%

OFFNUM
Real number (ℝ)

High correlation  Zeros 

Distinct9708
Distinct (%)12.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1410.45
Minimum0
Maximum10535
Zeros4577
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:06.108649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median143
Q31616
95-th percentile7774
Maximum10535
Range10535
Interquartile range (IQR)1605

Descriptive statistics

Standard deviation2442.1824
Coefficient of variation (CV)1.7314917
Kurtosis3.0629177
Mean1410.45
Median Absolute Deviation (MAD)142
Skewness2.0075251
Sum1.121886 × 108
Variance5964254.9
MonotonicityNot monotonic
2025-06-03T16:15:06.250857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4577
 
5.8%
1 2512
 
3.2%
2 2319
 
2.9%
3 1959
 
2.5%
4 1689
 
2.1%
5 1471
 
1.8%
6 1282
 
1.6%
7 1123
 
1.4%
8 998
 
1.3%
9 897
 
1.1%
Other values (9698) 60714
76.3%
ValueCountFrequency (%)
0 4577
5.8%
1 2512
3.2%
2 2319
2.9%
3 1959
2.5%
4 1689
 
2.1%
5 1471
 
1.8%
6 1282
 
1.6%
7 1123
 
1.4%
8 998
 
1.3%
9 897
 
1.1%
ValueCountFrequency (%)
10535 1
< 0.1%
10534 1
< 0.1%
10533 1
< 0.1%
10532 1
< 0.1%
10531 1
< 0.1%
10530 1
< 0.1%
10529 1
< 0.1%
10528 1
< 0.1%
10527 1
< 0.1%
10526 1
< 0.1%

RUNDATE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size621.6 KiB
45401.0
79541 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters556787
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45401.0
2nd row45401.0
3rd row45401.0
4th row45401.0
5th row45401.0

Common Values

ValueCountFrequency (%)
45401.0 79541
> 99.9%
(Missing) 1
 
< 0.1%

Length

2025-06-03T16:15:06.370186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:15:06.434122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
45401.0 79541
100.0%

Most occurring characters

ValueCountFrequency (%)
4 159082
28.6%
0 159082
28.6%
5 79541
14.3%
1 79541
14.3%
. 79541
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 556787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 159082
28.6%
0 159082
28.6%
5 79541
14.3%
1 79541
14.3%
. 79541
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 556787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 159082
28.6%
0 159082
28.6%
5 79541
14.3%
1 79541
14.3%
. 79541
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 556787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 159082
28.6%
0 159082
28.6%
5 79541
14.3%
1 79541
14.3%
. 79541
14.3%

SERVTYPE
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.734791
Minimum11
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:06.493380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q311
95-th percentile13
Maximum99
Range88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6552091
Coefficient of variation (CV)0.31148481
Kurtosis210.17446
Mean11.734791
Median Absolute Deviation (MAD)0
Skewness10.826712
Sum933397
Variance13.360553
MonotonicityNot monotonic
2025-06-03T16:15:06.591014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11 72967
91.7%
12 2575
 
3.2%
23 1537
 
1.9%
24 892
 
1.1%
29 359
 
0.5%
27 310
 
0.4%
21 249
 
0.3%
30 178
 
0.2%
13 177
 
0.2%
26 154
 
0.2%
Other values (4) 143
 
0.2%
ValueCountFrequency (%)
11 72967
91.7%
12 2575
 
3.2%
13 177
 
0.2%
21 249
 
0.3%
22 4
 
< 0.1%
23 1537
 
1.9%
24 892
 
1.1%
25 28
 
< 0.1%
26 154
 
0.2%
27 310
 
0.4%
ValueCountFrequency (%)
99 50
 
0.1%
30 178
 
0.2%
29 359
 
0.5%
28 61
 
0.1%
27 310
 
0.4%
26 154
 
0.2%
25 28
 
< 0.1%
24 892
1.1%
23 1537
1.9%
22 4
 
< 0.1%

SERVTYPE_DESC
Categorical

High correlation  Imbalance 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
FULL SERVICE - BRICK AND MORTAR
72967 
FULL SERVICE - RETAIL
 
2575
LIMITED SERVICE - DRIVE THRU/DETACHED FACILITY
 
1537
LIMITED SERVICE - LOAN PRODUCTION
 
892
LIMITED SERVICE - MOBILE/SEASONAL
 
359
Other values (10)
 
1212

Length

Max length46
Median length31
Mean length30.953521
Min length13

Characters and Unicode

Total characters2462105
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFULL SERVICE - BRICK AND MORTAR
2nd rowFULL SERVICE - BRICK AND MORTAR
3rd rowFULL SERVICE - BRICK AND MORTAR
4th rowFULL SERVICE - BRICK AND MORTAR
5th rowFULL SERVICE - BRICK AND MORTAR

Common Values

ValueCountFrequency (%)
FULL SERVICE - BRICK AND MORTAR 72967
91.7%
FULL SERVICE - RETAIL 2575
 
3.2%
LIMITED SERVICE - DRIVE THRU/DETACHED FACILITY 1537
 
1.9%
LIMITED SERVICE - LOAN PRODUCTION 892
 
1.1%
LIMITED SERVICE - MOBILE/SEASONAL 359
 
0.5%
LIMITED SERVICE - MESSENGER 310
 
0.4%
LIMITED SERVICE - ADMINISTRATIVE 249
 
0.3%
LIMITED SERVICE - TRUST 178
 
0.2%
FULL SERVICE - HOME BANKING 177
 
0.2%
LIMITED SERVICE - CONTRACTUAL 154
 
0.2%
Other values (5) 144
 
0.2%

Length

2025-06-03T16:15:06.711085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
service 79541
17.0%
79541
17.0%
full 75719
16.2%
brick 72967
15.6%
and 72967
15.6%
mortar 72967
15.6%
limited 3822
 
0.8%
retail 2636
 
0.6%
facility 1541
 
0.3%
drive 1537
 
0.3%
Other values (16) 5086
 
1.1%

Most occurring characters

ValueCountFrequency (%)
388782
15.8%
R 306096
12.4%
E 172384
 
7.0%
I 169668
 
6.9%
L 161205
 
6.5%
C 156943
 
6.4%
A 154295
 
6.3%
T 86177
 
3.5%
D 82570
 
3.4%
S 81336
 
3.3%
Other values (15) 702649
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2462105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
388782
15.8%
R 306096
12.4%
E 172384
 
7.0%
I 169668
 
6.9%
L 161205
 
6.5%
C 156943
 
6.4%
A 154295
 
6.3%
T 86177
 
3.5%
D 82570
 
3.4%
S 81336
 
3.3%
Other values (15) 702649
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2462105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
388782
15.8%
R 306096
12.4%
E 172384
 
7.0%
I 169668
 
6.9%
L 161205
 
6.5%
C 156943
 
6.4%
A 154295
 
6.3%
T 86177
 
3.5%
D 82570
 
3.4%
S 81336
 
3.3%
Other values (15) 702649
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2462105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
388782
15.8%
R 306096
12.4%
E 172384
 
7.0%
I 169668
 
6.9%
L 161205
 
6.5%
C 156943
 
6.4%
A 154295
 
6.3%
T 86177
 
3.5%
D 82570
 
3.4%
S 81336
 
3.3%
Other values (15) 702649
28.5%

STALP
Text

Missing 

Distinct59
Distinct (%)0.1%
Missing803
Missing (%)1.0%
Memory size621.6 KiB
2025-06-03T16:15:06.952026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.0000381
Min length2

Characters and Unicode

Total characters157481
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowMO
ValueCountFrequency (%)
tx 6359
 
8.1%
ca 5770
 
7.3%
fl 4349
 
5.5%
ny 4151
 
5.3%
il 3721
 
4.7%
pa 3473
 
4.4%
oh 3127
 
4.0%
nj 2350
 
3.0%
mi 2177
 
2.8%
mo 2153
 
2.7%
Other values (49) 41109
52.2%
2025-06-03T16:15:07.270697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 23243
14.8%
N 16661
10.6%
I 11738
 
7.5%
C 11560
 
7.3%
M 11436
 
7.3%
L 10823
 
6.9%
T 10498
 
6.7%
O 8743
 
5.6%
X 6359
 
4.0%
Y 5881
 
3.7%
Other values (14) 40539
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157481
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 23243
14.8%
N 16661
10.6%
I 11738
 
7.5%
C 11560
 
7.3%
M 11436
 
7.3%
L 10823
 
6.9%
T 10498
 
6.7%
O 8743
 
5.6%
X 6359
 
4.0%
Y 5881
 
3.7%
Other values (14) 40539
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157481
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 23243
14.8%
N 16661
10.6%
I 11738
 
7.5%
C 11560
 
7.3%
M 11436
 
7.3%
L 10823
 
6.9%
T 10498
 
6.7%
O 8743
 
5.6%
X 6359
 
4.0%
Y 5881
 
3.7%
Other values (14) 40539
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157481
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 23243
14.8%
N 16661
10.6%
I 11738
 
7.5%
C 11560
 
7.3%
M 11436
 
7.3%
L 10823
 
6.9%
T 10498
 
6.7%
O 8743
 
5.6%
X 6359
 
4.0%
Y 5881
 
3.7%
Other values (14) 40539
25.7%

STCNTY
Real number (ℝ)

High correlation  Zeros 

Distinct3198
Distinct (%)4.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean28538.94
Minimum0
Maximum78030
Zeros803
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:07.396383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5085
Q116001
median28115
Q342017
95-th percentile53031
Maximum78030
Range78030
Interquartile range (IQR)26016

Descriptive statistics

Standard deviation15864.222
Coefficient of variation (CV)0.55587988
Kurtosis-1.0749524
Mean28538.94
Median Absolute Deviation (MAD)13894
Skewness0.0097804197
Sum2.2700158 × 109
Variance2.5167355 × 108
MonotonicityNot monotonic
2025-06-03T16:15:07.596178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6037 1466
 
1.8%
17031 1155
 
1.5%
48201 885
 
1.1%
0 803
 
1.0%
4013 666
 
0.8%
48113 597
 
0.8%
12086 581
 
0.7%
6059 557
 
0.7%
36061 551
 
0.7%
6073 469
 
0.6%
Other values (3188) 71811
90.3%
ValueCountFrequency (%)
0 803
1.0%
1001 15
 
< 0.1%
1003 89
 
0.1%
1005 10
 
< 0.1%
1007 7
 
< 0.1%
1009 14
 
< 0.1%
1011 2
 
< 0.1%
1013 9
 
< 0.1%
1015 29
 
< 0.1%
1017 7
 
< 0.1%
ValueCountFrequency (%)
78030 9
< 0.1%
78020 2
 
< 0.1%
78010 9
< 0.1%
72153 4
< 0.1%
72151 2
 
< 0.1%
72149 1
 
< 0.1%
72147 1
 
< 0.1%
72145 3
 
< 0.1%
72143 1
 
< 0.1%
72141 1
 
< 0.1%

STNAME
Text

Missing 

Distinct59
Distinct (%)0.1%
Missing803
Missing (%)1.0%
Memory size621.6 KiB
2025-06-03T16:15:07.983650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length24
Mean length8.3739697
Min length4

Characters and Unicode

Total characters659358
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIllinois
2nd rowIllinois
3rd rowIllinois
4th rowIllinois
5th rowMissouri
ValueCountFrequency (%)
new 7309
 
8.0%
texas 6359
 
6.9%
california 5770
 
6.3%
florida 4349
 
4.7%
york 4151
 
4.5%
illinois 3721
 
4.1%
pennsylvania 3473
 
3.8%
carolina 3216
 
3.5%
ohio 3127
 
3.4%
north 2456
 
2.7%
Other values (60) 47765
52.1%
2025-06-03T16:15:08.491111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 82914
12.6%
i 71373
 
10.8%
n 57355
 
8.7%
s 52334
 
7.9%
o 51967
 
7.9%
e 43409
 
6.6%
r 37085
 
5.6%
l 30365
 
4.6%
t 17740
 
2.7%
h 15700
 
2.4%
Other values (38) 199116
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 659358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 82914
12.6%
i 71373
 
10.8%
n 57355
 
8.7%
s 52334
 
7.9%
o 51967
 
7.9%
e 43409
 
6.6%
r 37085
 
5.6%
l 30365
 
4.6%
t 17740
 
2.7%
h 15700
 
2.4%
Other values (38) 199116
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 659358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 82914
12.6%
i 71373
 
10.8%
n 57355
 
8.7%
s 52334
 
7.9%
o 51967
 
7.9%
e 43409
 
6.6%
r 37085
 
5.6%
l 30365
 
4.6%
t 17740
 
2.7%
h 15700
 
2.4%
Other values (38) 199116
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 659358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 82914
12.6%
i 71373
 
10.8%
n 57355
 
8.7%
s 52334
 
7.9%
o 51967
 
7.9%
e 43409
 
6.6%
r 37085
 
5.6%
l 30365
 
4.6%
t 17740
 
2.7%
h 15700
 
2.4%
Other values (38) 199116
30.2%

UNINUM
Real number (ℝ)

High correlation 

Distinct79541
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean305329.74
Minimum3
Maximum663907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:08.681687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6214
Q1198678
median263437
Q3464262
95-th percentile627297
Maximum663907
Range663904
Interquartile range (IQR)265584

Descriptive statistics

Standard deviation191723.6
Coefficient of variation (CV)0.62792309
Kurtosis-0.95377428
Mean305329.74
Median Absolute Deviation (MAD)168927
Skewness0.08674046
Sum2.4286233 × 1010
Variance3.6757937 × 1010
MonotonicityNot monotonic
2025-06-03T16:15:08.892205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
629595 1
 
< 0.1%
223055 1
 
< 0.1%
232078 1
 
< 0.1%
466427 1
 
< 0.1%
9231 1
 
< 0.1%
429739 1
 
< 0.1%
641411 1
 
< 0.1%
641392 1
 
< 0.1%
632193 1
 
< 0.1%
621883 1
 
< 0.1%
Other values (79531) 79531
> 99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
ValueCountFrequency (%)
663907 1
< 0.1%
663906 1
< 0.1%
663905 1
< 0.1%
663904 1
< 0.1%
663898 1
< 0.1%
663897 1
< 0.1%
663896 1
< 0.1%
663886 1
< 0.1%
663885 1
< 0.1%
663876 1
< 0.1%

ZIP
Real number (ℝ)

High correlation 

Distinct18284
Distinct (%)23.0%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean48949.246
Minimum0
Maximum99929
Zeros794
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:15:09.103119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4602.3
Q127540
median47715
Q373529
95-th percentile94591
Maximum99929
Range99929
Interquartile range (IQR)45989

Descriptive statistics

Standard deviation28237.231
Coefficient of variation (CV)0.57686753
Kurtosis-1.1186076
Mean48949.246
Median Absolute Deviation (MAD)24111
Skewness0.041589
Sum3.8933741 × 109
Variance7.9734121 × 108
MonotonicityNot monotonic
2025-06-03T16:15:09.334780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 794
 
1.0%
10022 56
 
0.1%
33134 47
 
0.1%
75225 46
 
0.1%
85016 43
 
0.1%
33131 43
 
0.1%
72401 41
 
0.1%
37027 41
 
0.1%
17601 40
 
0.1%
76107 40
 
0.1%
Other values (18274) 78348
98.5%
ValueCountFrequency (%)
0 794
1.0%
100 1
 
< 0.1%
601 1
 
< 0.1%
602 2
 
< 0.1%
603 4
 
< 0.1%
604 1
 
< 0.1%
606 1
 
< 0.1%
610 1
 
< 0.1%
612 5
 
< 0.1%
617 2
 
< 0.1%
ValueCountFrequency (%)
99929 2
 
< 0.1%
99921 2
 
< 0.1%
99901 6
< 0.1%
99840 1
 
< 0.1%
99835 4
< 0.1%
99833 2
 
< 0.1%
99827 1
 
< 0.1%
99801 9
< 0.1%
99780 1
 
< 0.1%
99762 2
 
< 0.1%

Interactions

2025-06-03T16:11:48.536258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:04.223155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:06.901222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:09.608444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:11.971353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:14.781782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:16.889377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:19.573522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:22.200389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:24.848082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:27.073941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:29.380087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:32.024959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:35.228774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:37.484198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:39.689287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:41.803564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:44.632140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:48.650891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:04.372996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:07.076631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:09.735033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:12.105611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:14.892363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:17.004324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:19.773600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:22.320273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:24.983455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:27.199528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:29.500189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:32.187063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:35.348040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:37.622372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:39.799874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:41.932871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:45.231372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:48.765777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:04.483604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:07.249578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:09.860190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:12.242327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:15.012714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:17.123189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:19.953929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:22.918398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:25.093152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:27.323058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:29.617975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-03T16:11:41.195866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:43.512779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:47.894662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:50.262369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:06.215786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:09.149111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:11.456132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:14.265926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:16.410885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:18.880232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:21.700629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:24.361500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:26.562494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:28.852269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:31.323079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:34.756953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:36.987774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:39.193143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:41.317532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:43.638348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:48.016571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:50.381404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:06.374341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:09.267869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:11.581266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:14.402766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:16.531975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:19.049366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:21.827600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:24.475719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:26.675331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:28.966178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:31.492442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:34.866111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:37.106546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:39.309063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:41.434937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:43.815997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:48.151609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:50.505965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:06.557140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:09.385159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:11.717227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:14.532704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:16.649408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:19.237995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:21.951051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:24.604944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:26.810462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:29.119349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:31.670862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:34.986269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:37.237274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:39.433090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:41.556007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:44.039803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:48.289858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:50.630711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:06.727349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:09.501114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:11.844434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:14.654712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:16.765865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:19.411526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:22.079468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:24.726171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:26.935834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:29.248608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:31.849337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:35.112155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:37.363267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:39.557003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:41.673016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:44.235693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-03T16:11:48.416508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-03T16:15:09.554885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACQDATEBKCLASSCBSA_DIVCBSA_DIV_FLGCBSA_DIV_NOCBSA_METROCBSA_METRO_FLGCBSA_MICRO_FLGCBSA_NOCERTCSA_FLGCSA_NOFI_UNINUMIDLATITUDELONGITUDEMAINOFFMDI_STATUS_DESCOBJECTIDOFFNUMSERVTYPESERVTYPE_DESCSTCNTYUNINUMXYZIP
ACQDATE1.0000.1600.1850.1320.0180.0030.1060.081-0.0060.2000.076-0.0110.2020.326-0.1170.0081.0000.2810.0190.0120.0440.071-0.0350.3260.008-0.1170.018
BKCLASS0.1601.0000.4100.1910.1200.0850.1150.1150.0750.3090.1140.0960.0610.0970.0580.0620.1790.4780.1520.2210.0480.3840.1000.0970.0620.0580.158
CBSA_DIV0.1850.4101.0001.0000.9990.9991.0001.0000.9990.2211.0000.9990.1030.1380.7640.6070.0770.4590.1680.1920.0990.2980.9040.1380.6070.7640.927
CBSA_DIV_FLG0.1320.1911.0001.0001.0000.5130.3590.1620.4630.1270.3910.5070.0790.0950.0900.1780.0570.2250.1070.1660.0410.0510.3240.0950.1780.0900.371
CBSA_DIV_NO0.0180.1200.9991.0001.0000.9731.0001.0000.9730.0261.0000.9730.0250.1600.2710.4150.1040.3000.0300.253-0.0260.0460.5350.1600.4150.271-0.422
CBSA_METRO0.0030.0850.9990.5130.9731.0001.0000.4520.8210.0350.7120.8390.0330.1250.0670.2730.0920.2850.0290.155-0.0140.0570.3560.1250.2730.067-0.275
CBSA_METRO_FLG0.1060.1151.0000.3591.0001.0001.0000.4520.7920.0860.6940.8720.0570.1630.1390.3560.0900.4540.0580.1430.0380.0610.5410.1630.3560.1390.387
CBSA_MICRO_FLG0.0810.1151.0000.1621.0000.4520.4521.0000.2460.0790.0590.2070.0370.1150.1010.0490.0670.2880.1040.1490.0420.0650.1800.1150.0490.1010.159
CBSA_NO-0.0060.0750.9990.4630.9730.8210.7920.2461.0000.0580.7390.9050.0570.0660.1040.2520.0630.2870.0350.052-0.0010.0440.4140.0660.2520.104-0.262
CERT0.2000.3090.2210.1270.0260.0350.0860.0790.0581.0000.0680.0290.9990.1300.0800.0940.1130.430-0.048-0.4400.0520.0820.0860.1300.0940.080-0.093
CSA_FLG0.0760.1141.0000.3911.0000.7120.6940.0590.7390.0681.0001.0000.0430.1220.0700.3450.0690.4050.0660.1130.0260.0450.5110.1220.3450.0700.377
CSA_NO-0.0110.0960.9990.5070.9730.8390.8720.2070.9050.0291.0001.0000.0300.0880.1230.2790.0720.3400.0510.105-0.0020.0650.4720.0880.2790.123-0.267
FI_UNINUM0.2020.0610.1030.0790.0250.0330.0570.0370.0570.9990.0430.0301.0000.1290.0770.0900.1100.396-0.048-0.4380.0520.0310.0870.1290.0900.077-0.088
ID0.3260.0970.1380.0950.1600.1250.1630.1150.0660.1300.1220.0880.1291.000-0.1380.0560.4680.141-0.0020.1120.1680.103-0.0091.0000.056-0.138-0.001
LATITUDE-0.1170.0580.7640.0900.2710.0670.1390.1010.1040.0800.0700.1230.077-0.1381.000-0.0700.0760.3400.005-0.101-0.0240.0570.196-0.138-0.0701.000-0.136
LONGITUDE0.0080.0620.6070.1780.4150.2730.3560.0490.2520.0940.3450.2790.0900.056-0.0701.0000.0800.382-0.0180.047-0.0190.0800.0880.0561.000-0.070-0.838
MAINOFF1.0000.1790.0770.0570.1040.0920.0900.0670.0630.1130.0690.0720.1100.4680.0760.0801.0000.2680.0970.1610.0550.0730.0990.4680.0800.0760.141
MDI_STATUS_DESC0.2810.4780.4590.2250.3000.2850.4540.2880.2870.4300.4050.3400.3960.1410.3400.3820.2681.0000.2921.0000.1560.3740.3980.1410.3820.3400.356
OBJECTID0.0190.1520.1680.1070.0300.0290.0580.1040.035-0.0480.0660.051-0.048-0.0020.005-0.0180.0970.2921.0000.1350.0350.0750.043-0.002-0.0180.005-0.002
OFFNUM0.0120.2210.1920.1660.2530.1550.1430.1490.052-0.4400.1130.105-0.4380.112-0.1010.0470.1611.0000.1351.000-0.0230.057-0.0870.1120.047-0.101-0.044
SERVTYPE0.0440.0480.0990.041-0.026-0.0140.0380.042-0.0010.0520.026-0.0020.0520.168-0.024-0.0190.0550.1560.035-0.0231.0001.0000.0280.168-0.019-0.0240.039
SERVTYPE_DESC0.0710.3840.2980.0510.0460.0570.0610.0650.0440.0820.0450.0650.0310.1030.0570.0800.0730.3740.0750.0571.0001.0000.0730.1030.0800.0570.059
STCNTY-0.0350.1000.9040.3240.5350.3560.5410.1800.4140.0860.5110.4720.087-0.0090.1960.0880.0990.3980.043-0.0870.0280.0731.000-0.0090.0880.196-0.101
UNINUM0.3260.0970.1380.0950.1600.1250.1630.1150.0660.1300.1220.0880.1291.000-0.1380.0560.4680.141-0.0020.1120.1680.103-0.0091.0000.056-0.138-0.001
X0.0080.0620.6070.1780.4150.2730.3560.0490.2520.0940.3450.2790.0900.056-0.0701.0000.0800.382-0.0180.047-0.0190.0800.0880.0561.000-0.070-0.838
Y-0.1170.0580.7640.0900.2710.0670.1390.1010.1040.0800.0700.1230.077-0.1381.000-0.0700.0760.3400.005-0.101-0.0240.0570.196-0.138-0.0701.000-0.136
ZIP0.0180.1580.9270.371-0.422-0.2750.3870.159-0.262-0.0930.377-0.267-0.088-0.001-0.136-0.8380.1410.356-0.002-0.0440.0390.059-0.101-0.001-0.838-0.1361.000

Missing values

2025-06-03T16:11:50.941990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-03T16:11:51.618953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-03T16:11:52.985533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

XYOBJECTIDACQDATEADDRESSADDRESS2BKCLASSCBSACBSA_DIVCBSA_DIV_FLGCBSA_DIV_NOCBSA_METROCBSA_METRO_FLGCBSA_METRO_NAMECBSA_MICRO_FLGCBSA_NOCERTCITYCOUNTYCSACSA_FLGCSA_NOESTYMDFI_UNINUMIDLATITUDELONGITUDEMAINOFFMDI_STATUS_CODEMDI_STATUS_DESCNAMEOFFNAMEOFFNUMRUNDATESERVTYPESERVTYPE_DESCSTALPSTCNTYSTNAMEUNINUMZIP
0-89.55491038.684286142539.018001 Saint Rose RdNaNSMSt. Louis, MO-ILNaN0.0NaN41180.01.0St. Louis, MO-IL0.041180.014761.0BreeseClintonSt. Louis-St. Charles-Farmington, MO-IL1.0476.0309649231.0223055.038.684286-89.5549100.0NaNNaN1NB BankST. ROSE FACILITY BRANCH3.045401.011.0FULL SERVICE - BRICK AND MORTARIL17027.0Illinois223055.062230.0
1-89.37221538.6181822NaN1350 12th StNaNSMSt. Louis, MO-ILNaN0.0NaN41180.01.0St. Louis, MO-IL0.041180.014761.0CarlyleClintonSt. Louis-St. Charles-Farmington, MO-IL1.0476.0253739231.0232078.038.618182-89.3722150.0NaNNaN1NB Bank1350 12TH STREET FACILITY1.045401.011.0FULL SERVICE - BRICK AND MORTARIL17027.0Illinois232078.062231.0
2-89.61233838.6117313NaN500 W Harrison StNaNSMSt. Louis, MO-ILNaN0.0NaN41180.01.0St. Louis, MO-IL0.041180.014761.0AvistonClintonSt. Louis-St. Charles-Farmington, MO-IL1.0476.0391049231.0466427.038.611731-89.6123380.0NaNNaN1NB BankAVISTON BRANCH2.045401.011.0FULL SERVICE - BRICK AND MORTARIL17027.0Illinois466427.062216.0
3-89.36902338.6112484NaN891 Fairfax StNaNSMSt. Louis, MO-ILNaN0.0NaN41180.01.0St. Louis, MO-IL0.041180.014761.0CarlyleClintonSt. Louis-St. Charles-Farmington, MO-IL1.0476.001/01/18789231.09231.038.611248-89.3690231.0NaNNaN1NB Bank1NB Bank0.045401.011.0FULL SERVICE - BRICK AND MORTARIL17027.0Illinois9231.062231.0
4-90.65598038.7970825NaN240 Salt Lick RdNaNNMSt. Louis, MO-ILNaN0.0NaN41180.01.0St. Louis, MO-IL0.041180.057899.0Saint PetersSt. CharlesSt. Louis-St. Charles-Farmington, MO-IL1.0476.038383429739.0429739.038.797082-90.6559801.0NaNNaN1st Advantage Bank1st Advantage Bank0.045401.011.0FULL SERVICE - BRICK AND MORTARMO29183.0Missouri429739.063376.0
5-114.77051632.489945640960.0645 N William Brooks AveNaNNMNaNNaN0.00.00.00.0NaN0.00.057298.0San LuisYumaNaN0.00.035216360755.0251437.032.489945-114.7705160.0NaNNaN1st Bank YumaSAN LUIS BRANCH4.045401.011.0FULL SERVICE - BRICK AND MORTARAZ4027.0Arizona251437.085336.0
6-114.62446932.6767377NaN2799 S 4th AveNaNNMNaNNaN0.00.00.00.0NaN0.00.057298.0YumaYumaNaN0.00.037138360755.0360755.032.676737-114.6244691.0NaNNaN1st Bank Yuma1st Bank Yuma0.045401.011.0FULL SERVICE - BRICK AND MORTARAZ4027.0Arizona360755.085364.0
7-114.62478932.694539842251.01800 S 4th AveNaNNMNaNNaN0.00.00.00.0NaN0.00.057298.0YumaYumaNaN0.00.039329360755.0467596.032.694539-114.6247890.0NaNNaN1st Bank YumaYUMA BRANCH5.045401.011.0FULL SERVICE - BRICK AND MORTARAZ4027.0Arizona467596.085364.0
8-110.93101731.347570942223.0825 N Grand AveSte 104NMNaNNaN0.00.00.00.0NaN0.00.057298.0NogalesSanta CruzNaN0.00.039352360755.0468263.031.347570-110.9310170.0NaNNaN1st Bank YumaNOGALES BRANCH6.045401.011.0FULL SERVICE - BRICK AND MORTARAZ4023.0Arizona468263.085621.0
9-114.44417832.66044210NaN11600 S Fortuna RdNaNNMNaNNaN0.00.00.00.0NaN0.00.057298.0YumaYumaNaN0.00.039790360755.0493457.032.660442-114.4441780.0NaNNaN1st Bank YumaFortuna Branch3.045401.013.0FULL SERVICE - HOME BANKINGAZ4027.0Arizona493457.085367.0
XYOBJECTIDACQDATEADDRESSADDRESS2BKCLASSCBSACBSA_DIVCBSA_DIV_FLGCBSA_DIV_NOCBSA_METROCBSA_METRO_FLGCBSA_METRO_NAMECBSA_MICRO_FLGCBSA_NOCERTCITYCOUNTYCSACSA_FLGCSA_NOESTYMDFI_UNINUMIDLATITUDELONGITUDEMAINOFFMDI_STATUS_CODEMDI_STATUS_DESCNAMEOFFNAMEOFFNUMRUNDATESERVTYPESERVTYPE_DESCSTALPSTCNTYSTNAMEUNINUMZIP
795320.0000000.0000007953343952.0301 N Pine StNaNNMDeRidder, LANaN0.0NaN0.00.0NaN1.019760.058228.0DeridderBeauregardDeRidder-Fort Polk South, LA1.0217.005/28/1928443550.06747.00.0000000.0000000.0NaNNaNb1BANKDeridder Banking Cener Branch71.045401.011.0FULL SERVICE - BRICK AND MORTARLA22011.0Louisiana6747.070634.0
795330.0000000.00000079534NaN1300 W Tunnel BlvdNaNNMHouma-Thibodaux, LANaN0.0NaN26380.01.0Houma-Thibodaux, LA0.026380.058228.0HoumaTerrebonneNaN0.0NaN05/01/2020443550.073828.00.0000000.0000000.0NaNNaNb1BANKHouma - W Tunnel Blvd Banking Center Branch79.045401.011.0FULL SERVICE - BRICK AND MORTARLA22109.0Louisiana73828.070360.0
79534-116.79263247.68119879535NaN912 Northwest BlvdNaNNMCoeur d'Alene, IDNaN0.0NaN17660.01.0Coeur d'Alene, ID0.017660.057074.0Coeur D AleneKootenaiSpokane-Spokane Valley-Coeur d'Alene, WA-ID1.0518.005/17/2001356862.0356862.047.681198-116.7926321.0NaNNaNbankcdabankcda0.045401.011.0FULL SERVICE - BRICK AND MORTARID16055.0Idaho356862.083814.0
79535-116.12444247.5376937953641509.0120 Railroad AveNaNNMNaNNaN0.00.00.00.0NaN0.00.057074.0KelloggShoshoneNaN0.00.009/18/2000356862.0360910.047.537693-116.1244420.0NaNNaNbankcdaKellogg Branch4.045401.011.0FULL SERVICE - BRICK AND MORTARID16079.0Idaho360910.083837.0
79536-116.78850147.75875079537NaN162 W Hayden AveNaNNMCoeur d'Alene, IDNaN0.0NaN17660.01.0Coeur d'Alene, ID0.017660.057074.0HaydenKootenaiSpokane-Spokane Valley-Coeur d'Alene, WA-ID1.0518.004/13/2015356862.0567663.047.758750-116.7885010.0NaNNaNbankcdaHayden Branch5.045401.011.0FULL SERVICE - BRICK AND MORTARID16055.0Idaho567663.083835.0
79537-116.93585047.71403979538NaN922 E Polston AveNaNNMCoeur d'Alene, IDNaN0.0NaN17660.01.0Coeur d'Alene, ID0.017660.057074.0Post FallsKootenaiSpokane-Spokane Valley-Coeur d'Alene, WA-ID1.0518.009/10/2019356862.0616993.047.714039-116.9358500.0NaNNaNbankcdaPost Falls Branch6.045401.011.0FULL SERVICE - BRICK AND MORTARID16055.0Idaho616993.083854.0
795380.0000000.00000079539NaN120 Railroad AveNaNNMNaNNaN0.00.00.00.0NaN0.00.057074.0KelloggShoshoneNaN0.00.003/21/2022356862.0651678.00.0000000.0000000.0NaNNaNbankcdaMessenger Service Branch7.045401.027.0LIMITED SERVICE - MESSENGERID16079.0Idaho651678.083837.0
79539-92.95297540.26695579540NaN1 S Lincoln StNaNNMNaNNaN0.00.00.00.0NaN0.00.016103.0Green CitySullivanNaN0.00.011/21/194310320.010320.040.266955-92.9529751.0NaNNaNfarmbankfarmbank0.045401.011.0FULL SERVICE - BRICK AND MORTARMO29211.0Missouri10320.063545.0
795400.0000000.00000079541NaN701 N Pearl StNaNNMNaNNaN0.00.00.00.0NaN0.00.016103.0MilanSullivanNaN0.00.009/17/200710320.0468331.00.0000000.0000000.0NaNNaNfarmbankMilan Branch2.045401.011.0FULL SERVICE - BRICK AND MORTARMO29211.0Missouri468331.063556.0
79541-92.58335640.22871579542NaN3311 N Baltimore StNaNNMKirksville, MONaN0.0NaN0.00.0NaN1.028860.016103.0KirksvilleAdairNaN0.0NaN12/16/202010320.0629595.040.228715-92.5833560.0NaNNaNfarmbankKirksville Branch3.045401.011.0FULL SERVICE - BRICK AND MORTARMO29001.0Missouri629595.063501.0